SCTE-ISBE EMS Journal V1V2

JOURNAL OF
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ENERGY MANAGEMENT
Volume 1, Number 2
October 2016

JOURNAL OF ENERGY MANAGEMENT
VOLUME 1, NUMBER 2
OCTOBER 2016
Society of Cable Telecommunications Engineers, Inc.
International Society of Broadband Experts™
140 Philips Road, Exton, PA 19341-1318
© 2016 by the Society of Cable Telecommunications Engineers, Inc. All rights reserved.
As complied, arranged, modified, enhanced and edited, all license works and other separately owned
materials contained in this publication are subject to foregoing copyright notice. No part of this journal
shall be reproduced, stored in a retrieval system or transmitted by any means, electronic, mechanical,
photocopying, recording or otherwise, without written permission from the Society of Cable
Telecommunications Engineers, Inc. No patent liability is assumed with respect to the use of the
information contained herein. While every precaution has been taken in the preparation of the publication,
SCTE assumes no responsibility for errors or omissions. Neither is any liability assumed for damages
resulting from the use of the information contained herein.

Vol. 1, No. 2, October 2016 | ©2016 SCTE 3
Table of Contents
5 From the Editors
Utility Side Papers
6 IEEE 2.5 Beta Method Unraveled
Nathan Subramaniam, Manager, Industry Operations, Charter Communications
21 Trading with Utilities: Cable Facility & Customer Opportunities
Robert F. Cruickshank III, RA, Visiting Researcher,
University of Colorado at Boulder
Laurie F. Asperas-Valayer, Strategy Consultant, Le Savoir Faire
Dr. Ronald J. Rizzuto, Distinguished Professor, University of Denver
32 How Power Purchase Agreements Impact Your Energy Strategy
John W. duPont, P.E., Principal of Distributed Energy Resources, EnerNOC, Inc.
Alvaro E. Pereira, PhD, Director of Supply Advisory Services, EnerNOC, Inc.
Routing and Video Papers
50 Evaluating Alternative Broadband Architectures for Energy
Consumption
Tom Gonder, Chief Network Architect, Charter Communications
John B. Carlucci, Consultant, Petrichor Networks, LLC.
65 Power Efficiency Strategies in Modern Routers
Andrew Smith, Chief Architect for Cable, Juniper Networks
HFC & Facilities Papers
76 Giving HFC a Green Thumb
John Ulm Engineering Fellow, Broadband Systems
Zoran Maricevic, Engineering Fellow, Access Technologies
110 Achieving Energy Savings in Broadband Provider Edge
Facilities through a Portfolio Based Approach
Gregory J. Baron, BOC, Energy Solutions Manager, Hitachi Consulting
Supriya Dharkar, Senior Consultant, Engineering Services, Hitachi Consulting
George Gosko, CEM, LEED AP, Manager, Engineering Services,
Hitachi Consulting
Cassie Quaintance, MAS, CEM, LEED AP, Engineering Services Manager,
Hitachi Consulting
SCTE/ISBE Engineering
Committee Chair:
Bill Warga, VP- Technology,
Liberty Global
SCTE Member
SCTE/ISBE Energy Management
Subcommittee (EMS)
Committee Chair:
Dan Cooper
SCTE Member
Senior Editors
Dan Cooper
SCTE Member
Derek DiGiacomo
Senior Director- Information
Systems & Energy Management
Program, SCTE/ISBE
Publications Staff
Chris Bastian
SVP & Chief Technology Officer,
SCTE/ISBE
Dean Stoneback
Senior Director- Engineering &
Standards, SCTE/ISBE
Kim Cooney
Technical Editor, SCTE/ISBE

Batteries, Storage, IOT Papers
138 Overview of Current Battery and Energy Storage Technologies
Kathleen Ayuk, Master of Science Candidate in Sustainable Engineering
Villanova University
155 Methods to Maximize IoT Battery life
Joe Rodolico, Principal Engineer, Comcast
162 IoT Device Energy Harvesting Technologies and Implementations
Joe Rodolico, Principal Engineer, Comcast
Editorial Correspondence: If there are errors or omissions to the information provided
in this journal, corrections may be sent to our editorial department. Address to: SCTE Journals,
SCTE/ISBE, 140 Philips Road, Exton, PA 19341-1318 or email journals@scte.org.
Submissions: If you have ideas or topics for future journal articles, please let us know. Topics must be relevant to our membership
and fall under the technologies covered by each respective journal. All submissions will be peer reviewed and published at the
discretion of SCTE/ISBE. Electronic submissions are preferred, and should be submitted to SCTE Journals, SCTE/ISBE, 140 Philips
Road, Exton, PA 19341-1318 or email journals@scte.org.
Subscriptions: Access to technical journals is a benefit of SCTE/ISBE Membership. Nonmembers can join at www.scte.org/join.
SCTE/ISBE Engineering
Committee Chair:
Bill Warga, VP- Technology,
Liberty Global
SCTE Member
SCTE/ISBE Energy Management
Subcommittee (EMS)
Committee Chair:
Dan Cooper
SCTE Member
Senior Editors
Dan Cooper
SCTE Member
Derek DiGiacomo
Senior Director- Information
Systems & Energy Management
Program, SCTE/ISBE
Publications Staff
Chris Bastian
SVP & Chief Technology Officer,
SCTE/ISBE
Dean Stoneback
Senior Director- Engineering &
Standards, SCTE/ISBE
Kim Cooney
Technical Editor, SCTE/ISBE

From the Editors
Welcome to the second issue of the Journal of Energy Management, a publication of collected papers by
the Society of Cable Telecommunications Engineers (SCTE) and its global arm, the International Society
of Broadband Experts (ISBE).
This October 2016 edition of the Journal focuses on cost saving opportunities and various energy saving
methodologies, including:
• Financial considerations and risks of off-site alternative energy procurement strategies
• Assessment of changing utility rate structures which could provide financial opportunities for
cable operators
• Decoding the IEEE 1366 standard that impacts cable’s utility partners
• Measuring HVAC, lighting, energy generation and storage at the critical facility level
• The impact of batteries on the Internet of Things (IoT)
• HFC capacity planning and the impact of evolving architectures on cable plant power
• A deep dive into energy demands of network routers.
• Examination of energy storage with up and coming battery technologies
In support of Energy 2020, the Energy Management Subcommittee has worked diligently this year to
expand its team of participants as well as producing 19 standards and operational practices. These journal
entries can be further examined by respective working groups ultimately leading to approval by the ANSI
accredited SCTE ISBE Standards program.
We would like to thank the individuals who contributed to this issue of the Journal of Energy
Management, especially the authors, peer reviewers, and the SCTE/ISBE publications and marketing
staff. We hope that the selected papers spark innovative ideas to further our collaboration to mature the
industry’s operational practices, standards and technology solutions to help everyone meet the Energy
2020 goals.
In closing, if there is any editorial information or topics that you would like us to consider for the third
issue of SCTE/ISBE Journal of Energy Management, please refer to the “editorial correspondence” and
“submissions” sections at the bottom of the table of contents for further instructions.
SCTE/ISBE Journal of Energy Management Senior Editors,
Dan Cooper
SCTE Energy Management Subcommittee Chair
Derek DiGiacomo
Senior Director, Information Systems and Energy Management Program

IEEE 2.5 Beta Method Unraveled
A Technical Paper Prepared for SCTE/ISBE by
Nathan Subramaniam, Manager, Industry Operations, Charter Communications, SCTE/ISBE
Member
13820 Sunrise Valley Drive
Herndon, VA 20171
Nathan.Subramaniam@Charter.com
703-561-8947
Credits:
Nathan Subramaniam - Author
Lydia Brown – Editor and Designer
Emily Mcloughlin – Sustainability Program SME
Joe Arsenault – Metric SME

© 2016 Society of Cable Telecommunications Engineers, Inc. All rights reserved. 7
Table of Contents
Title Page Number
Table of Contents ____________________________________________________________________ 7
1. Introduction ____________________________________________________________________ 8
1.1. Problem Statement _______________________________________________________ 8
2. The Mystery____________________________________________________________________ 8
3. Discovering the Right Methodology _________________________________________________ 9
3.1. IEEE Working Group 2.5 Beta Methology ______________________________________ 9
3.2. Major Event Day Versus Day-To-Day Operations _______________________________ 10
4. Knowing What to Measure and How________________________________________________ 10
5. Putting the Pieces Together ______________________________________________________ 12
6. Balancing PUC Requirements ____________________________________________________ 13
6.1. PUC Mandates__________________________________________________________ 13
6.2. Smart Metering__________________________________________________________ 13
7. Some Implementation Q&A to Consider _____________________________________________ 14
8. Applying the 2.5 Beta Method_____________________________________________________ 15
9. Supplemental Information ________________________________________________________ 16
9.1. 2.5 Beta Methodology Example – Applied to NOC Managed Tickets ________________ 16
9.2. Customer Relationships by Power Company Boundary __________________________ 17
9.3. Weekly Reliability Metrics by Power Company _________________________________ 18
9.4. Commercial Power Outages – Analysis of Critical Infrastructure Hub Impacting Tickets _ 19
10. Bibliography and References _____________________________________________________ 19
11. Abbreviations__________________________________________________________________ 20
List of Figures
Title Page Number
Figure 1 - Major Event Day Versus Day-to-Day Operations 10
Figure 2 - Outage Performance (SAIDI) – Stacked Bar Chart 12
Figure 3 - Outage Performance (SAIDI) – Bar and Line Chart 12
Figure 4 - 2.5 Beta Methodology - Applied to NOC Managed Tickets 16
Figure 5 - Customer Relationship by Power Company Boundary 17
Figure 6 - Weekly Reliability Metrics by Power Company 18
Figure 7 - Commercial Power Outages - Analysis of Critical Infrastructure Hub Impacting Tickets 19
List of Tables
Title Page Number
Table 1 - Five Indices Used in Outage Reporting Utilizing 2.5 Beta Methodology Along with A Detailed
Description Of Each 10
Table 2 - Smart Grid Applications 13
Table 3 – Abbreviation Table 20

1. Introduction
This white paper, written for non-technical audiences, discusses the scope and applicability of the IEEE
(Institute of Electrical and Electronics Engineers) 1366 standard methodology. It focuses on solutions
available to improve utility indices and MTTX1
metrics. The purpose is to provide an educational and
informational white paper and not overload the audience with statistical formulas and complex details.
1.1. Problem Statement
Internal and external goals are set around reliability performance yet there has been no uniform
methodology for removing events that are so far away from normal performance (known as outliers).
Without removal of outliers, variation in annual performance is too great to set meaningful targets.
Outliers are removed manually today, so they don’t skew the stats, but criteria are variable. Normalizing
reliability data reduces the variability, thus making trending and goal setting possible.
One of the goals for this white paper is to illustrate the statistical concept and provide awareness.
Another goal is to be able to summarize concepts and its applications to facilitate socializing with a wider
audience such as Critical Infrastructure, Network Operations Centers (NOCs), and the Energy 2020
Program.
A solution developed by IEEE for power utilities contains the following attributes as detailed within this
white paper:
1. A statistics based methodology (referred to as the “Beta Method”) for identifying outlying
performance (otherwise known as “major event days” or “MEDs”).
2. Known as the “2.5 Beta Method” because of its use of the naturally occurring log-normal
distribution that best describes reliability performance data.
3. Using this method, we can calculate indices on both a normalized and unadjusted basis.
4. Normalized indices provide metrics that should be used for goal setting.
5. Unadjusted indices, when compared to the normalized indices, provide information about
performance during major events (identifies abnormal performance).
2. The Mystery
We know that outages and/or outage durations spike during MEDs, but is there a way to show a true
picture of outages without manually removing the MEDs (outliers) that skew Reliability Metrics? That is
the piece of the puzzle we set out to find. Metrics drives behavior, but what if the metrics don’t show the
whole story? This is when you need a good Data Analyst whose curiosity incites him or her to search for
and find the answers to such questions. In our case, we need to find out how we can accurately measure
outages, but also account for anomalies in a data set.
Having the ability to find truth in the metrics is what makes a brilliant analyst stand apart from the rest.
As mentioned in Now You See It by Stephen Few and explained by George Kuhn, former Director of
1
MTTX represents a subset of metrics with the “X” signifying a variable, i.e., MTTR (mean time to
restore), MTTA (mean time to acknowledge), etc.

Research Services at research and marketing strategies (RMS), good Data Analysts have 13 traits of
which I will mention a few that are relevant to this white paper:
1. Analytical – seems obvious, but it is “being able to take a percentage or a number and
‘decompose it’ into the sum of the parts that make it up”. Applying this trait to this white paper,
it is looking at outages and saying “what percent of those outages are on major event days?” and
finding the answer.
2. Capable of spotting patterns – “the ability to spot trends or themes in the data. Spotting the data
patterns takes a unique eye. Oftentimes, this doesn’t materialize until you can see it in graphical
format.” In our example it is in the realization that outage numbers and/or subscriber counts (in
specific markets) increase on major event days.
3. Curious – “the difference here is although you may be interested in a topic, you need to have the
curiosity to dig deeper into data. Don’t just report the 41% and move on, figure out why 41% is
41%.” How can we accurately report outages, but yet separate out abnormal performance that
skew the results?
4. Open-minded and flexible – be objective; “although you may have some preconceived notions
about how the data will turn out, be open-minded and be able to adjust your findings on the fly.”
This is evident in identifying the 2.5 Beta Methodology as a viable solution to tracking outages.
3. Discovering the Right Methodology
An IEEE Working Group comprising roughly 130 members developed the “2.5 Beta Methodology” in
IEEE Standards 1366-2003. Membership includes representation from utility companies, regulatory staff,
manufacturing companies, consultants and academicians. The Working Group evaluated Beta Multiplier
values using a number of power companies of different sizes and from different parts of the United States.
Though a number of alternative approaches were considered, 2.5 Beta Method came out to be superior.
We quickly realized this same methodology could be used in reporting Time Warner Cable (TWC)2
outages, specifically relevant to MEDs.
3.1. IEEE Working Group 2.5 Beta Methology
o Improves TWC’s ability to view day-to-day outage reliability performance without removing the
outliers that often distort it.
o Allows for consistent calculation of reliability performance by each TWC Market and/or Power
Company.
o Normalizes reliability data and reduces variability, thus making trending and goal setting
possible.
o Provides an objective (quantitative basis) for measuring and segmenting major events from
normal operations.
2
Time Warner Cable (TWC) was acquired by Charter Communications in May 2016.

3.2. Major Event Day Versus Day-To-Day Operations
This pictorial illustrates how Major Event Days are separate, albeit related to Day-to-Day Operations and
need to be considered in calculating outages.
All Outage Events
Day-to-Day
Operations
Major Event
Day
Targets set on Day-to-Day
Operations
Figure 1 - Major Event Day Versus Day-to-Day Operations
4. Knowing What to Measure and How
Table 1 - Five Indices Used in Outage Reporting Utilizing 2.5 Beta Methodology Along
with A Detailed Description Of Each
Metric Definition Formula Description
SAIDI
system
average
interruption
duration
index
Total duration
of interruption
(downtime
minutes) on an
average per
customer
Sum of (Max
of Outage
Duration
Minutes) /
Total Customer
Relationships
SAIDI is the index used to identify Major Event Days
(MEDs) and correlates with total cost of unreliability,
including utility repair costs and subscriber losses. SAIDI
is a quality factor taking into account the duration of
interruptions (locating and repairing faults faster). The use
of SAIDI approach also accounts for variation in
subscriber count, especially in the case of mergers (as in
Charter/Time Warner Cable), where the subscriber mix
tends to vary. This allows us to set realistic company-wide
reliability targets. An important point to note is that
SAIDI, excluding MEDs, is controllable.3
CAIDI
customer
average
interruption
duration
index
Average time
to restore
service due to a
power outage
Sum of (Max
of Outage
Duration
Minutes) / Sum
of (Max of
Subs impacted)
CAIDI correlates to value per Customer; CAIDI does not
reflect the size of power outage events. CAIDI is a more
granular index to help in the management of service
provider networks and decisions for reliability based
capital expenditures. Obtaining the correct number of subs
impacted by an outage event is of critical importance for
calculating customer based reliability indices such as
3
Daily SAIDI values are preferred to daily customer minutes of interruption (CMI) values for MED
identification because the SAIDI permits comparison and computation among years with different
numbers of customers served.

Metric Definition Formula Description
CAIDI, since only a fraction of interested customers will
call the utility.
SAIFI
system
average
interruption
frequency
index
Probability of
TWC
subscribers
experiencing a
power outage
during the time
frame under
study
SAIDI / CAIDI SAIFI is another quality factor that takes into account the
number of interruptions. SAIFI is a frequency based index
and not an indicator of the total cost of the outage. SAIFI
has a strong correlation with backup power (prevent faults
from occurring).
CIII
customers
interrupted
per
interruption
index
Average
number of
subscribers
interrupted
during a power
outage
Sum of (Max
of Subs
impacted) /
Number of
outage tickets
CIII allows the ability to drill down to number of
subscriber impacts per day and to determine if one outage
or multiple outages contributed to the vast majority of the
subscribers losing power.
ASAI
average
service
availability
index
Ratio of the
total number of
subscriber
hours that
service was
available
during a given
time period to
the total
subscriber
hours
demanded
[(10,080
minutes –
SAIDI)/10,080
minutes] *100,
if calculated
weekly
ASAI is the availability percentage; major event days must
be excluded from the ASAI calculation so it is easy to
implement corrective actions on normal day to day
occurrences.
a. Availability can be calculated daily, weekly, monthly, quarterly or yearly.
b. Yearly threshold does not account for seasonality and hence is less valuable.
All of the indices give a baseline for performance and give utilities a method for targeting improvement.
The targeting efforts must focus on preventing faults from occurring which impacts SAIFI or locating and
repairing faults faster which targets SAIDI.

5. Putting the Pieces Together
Both SAIDI charts below clearly show how MEDs impact outages in the legacy Time Warner Cable
world.4
Time Warner Cable merged with Charter Communications May 2016 and the data analysis is
based on pre-merger Time Warner Cable data.
Figure 2 - Outage Performance (SAIDI) – Stacked Bar Chart
Figure 3 - Outage Performance (SAIDI) – Bar and Line Chart
4
The Outage Performance (SAIDI) charts contain sensitive information that is protected and privileged
and must not be reproduced without owner’s permission.

6. Balancing PUC Requirements
6.1. PUC Mandates
State Public Utility Commissions (PUCs) interest in electricity continues to grow. In some states, PUCs
have imposed mandates on Power companies to comply with the reliability indices set forth by the IEEE
working group and have attached revenue incentives to performance. Utilities use these indices to
determine when an anomaly occurred on the system and to compare their performance against other
utilities.
PUCs set performance goals (utility cost per customer) and establish reward and penalty structure.
Utilities in turn strive to meet or exceed these goals or miss the goals and either receive financial
incentives or pay penalties. Some utilities have implemented flexi-watts that work using the concept of
supply and demand. The final outcome boils down to reduced costs to customers, reliable service and
increase in customer satisfaction scores.
Both Department of Energy (DOE) and North American Electric Reliability Corp (NERC) require
reporting of major electricity system incidents. Reporting to these national bodies is now mandatory,
required in real time, and includes incidents that sometimes result in no loss of electric service to
customers. The principal purpose of this form of reporting to national bodies is to provide information
on major electricity system emergencies that may require immediate responses by industry or
government to ensure public health and safety.5
California Public Utilities Commission (CPUC) and the National Association of Regulatory Utility
Commissioners (NARUC) have petitioned FCC for access to outage information that is filed by
Communications providers via the FCC Network Outage Reporting System (NORS). This data can
potentially help to enhance homeland security and emergency response functions. Additionally, it helps
states to evaluate the causes of the outages and to determine whether they are one time occurrence vs.
systematic failures. FCC is working on the best approach to facilitate this information sharing but at the
same time investigating options to safeguard sensitive customer data.
FCC released a declarative ruling recently that allows utilities to place robo-calls concerning service
outages, service restoration, meter work, tree trimming and other field work, threatened service
curtailment, and potential brown-outs due to heavy energy use. Utility callers must still comply with
Telephone Consumer Protection Act (TCPA) of 1991, including customer opt-out requirements and
ceasing robo-calls to numbers that have been reassigned to new customers.
6.2. Smart Metering
Table 2 - Smart Grid Applications
Smart grid Applications Primary impacts on outages
Fault detection and automated feeder
switching
Reduction in the frequency and duration of outages and the
number of affected customers
Diagnostic and equipment health
sensors
Reductions in the frequency of outages and the number of
affected customers
5
According to U.S. Energy Information Administration (EIA) data, 65 million households are served by
utilities that offer time varying rates such as Time of Use (TOU) rates where the utility provides a
discounted rate outside of peak times.

Smart grid Applications Primary impacts on outages
Outage detection and notification
systems
Reductions in the duration of outages
7. Some Implementation Q&A to Consider
Q1: Do these metrics help us make investment decisions by geography?
A: By enhancing power data with common language code sets (CLLI6
, CLEI7
, and PCN8
), we can:
• Correlate CLLI codes (location) to where network outages occur
• Map network equipment (CLEI) to a particular CLLI location
• Identify equipment that has a high rate of PCNs
• Display outage hotspot locations and Mean Time to Restore Service (MTRS) via dashboard
or heat map
• Predict potential outages and MTRS (with PCN correlation)
• Provide proactive recommendations on preventing potential future outage occurrences
The purpose of CLEI is to act upon Product Change Notification (PCN). Telecommunications
equipment is constantly changing and evolving. The changes range from product defect to a product
enhancement. The communication of these changes from an equipment supplier to an equipment user
is accomplished via a product change notice or PCN.
Q2: Are the SAIDIs controllable?
A: Since we have the ability to exclude major event days (MEDs) from normal operations,
normalized SAIDI must be used to control service quality. Events that exceed the MED threshold
must be analyzed separately, root cause analysis must be conducted and corrective measures must be
taken. 9
Q3: How can we better enhance the Commercial Power Summary report that will make sense to
executives?
A: By identifying Customer Relationships by power company (e.g., anything in the territory for Duke
Energy, etc. for each TWC subscriber), taking those addresses and geocoding them we should get a
pulse of reliability indices per power company to report on.
6
Common language location identifier (CLLI) is a standardized way of describing locations and pieces
of hardware in those locations.
7
Common language equipment identification (CLEI) is a part number of a piece of equipment (usually
Central Office equipment).
8
Product change notice (PCN) is a document issued by a manufacturer to inform customers about a
change to a network element.
9
Research shows that average customer may be dissatisfied if they are without electricity for more than 53
minutes a year. This correlates to the fact that a “four-nine” availability value translates into a SAIDI of 52
minutes per year. The median SAIDI value for North American utilities is approximately 90 minutes. It is
imperative therefore for the utilities to maintain that caliber of electric service and make that one of the core
facets of the utility’s business model.

8. Applying the 2.5 Beta Method
Key Takeaways:
1. Provide an indication of what percent of the overall downtime minutes (SAIDI), as a result of
power outages, are attributed to major events. A preferred MED threshold would be by month/by
market to account for seasonality in the data.
2. Triangulate SAIDI (interruption duration), CAIDI (interruption restoration time), and SAIFI
(interruption frequency) metrics to enable process improvement efforts.
3. Establish realistic MTTX targets via IEEE normalized CAIDI measurement to enable more
accurate NOC to NOC comparisons10
.
4. Apply ASAI index for availability calculation that factors in SAIDI with the ability to exclude
major events vs. using the conventional approach [(total circuit minutes in period – circuit
downtime minutes)/total circuit minutes)]*100.
5. Use normalized SAIDI to set realistic SAIDI goals and monitor reliability indices by acting on
real changes, thus reducing time wasted on chasing variances due to acts of God.
6. Analyze major event day tickets separately from normal operations. Nothing is excluded.
7. Determine Customer Relationships (CRs) by each power company boundary to enhance our
ability to compute reliability indices by each utility.
8. Collect findings drawn from publicly available “utility” reliability performance information
submitted to respective PUCs to understand evolving requirements by each state and any
variation to utility reporting practices with respect to IEEE 1366 standard.
9. Identify correlations (if any) between company specific reliability indices (SAIDI, CAIDI etc.)
for each utility vs the reliability indices reported by utilities to their respective State Public Utility
Commissions (PUCs).
10. Meet with business partners with the respective power companies to identify improvement areas.
10
By using unique Customer Relationships specific to each NOC.

9. Supplemental Information
9.1. 2.5 Beta Methodology Example – Applied to NOC Managed Tickets
Figure 4 - 2.5 Beta Methodology - Applied to NOC Managed Tickets
(The data presented on this report is for legacy Time Warner Cable)11
11
The chart above contains sensitive information that is protected and privileged and must not be
reproduced without owner’s permission.

9.2. Customer Relationships by Power Company Boundary
Figure 5 - Customer Relationship by Power Company Boundary
12

9.3. Weekly Reliability Metrics by Power Company
Figure 6 - Weekly Reliability Metrics by Power Company
Total Available Weekly Minutes = 10,080 minutes (24*60*7).
CR=Unique Customer Relationships
13

9.4. Commercial Power Outages – Analysis of Critical Infrastructure Hub
Impacting Tickets
Figure 7 - Commercial Power Outages - Analysis of Critical Infrastructure Hub Impacting
Tickets
10. Bibliography and References
Now You See It, Stephen Few and explained by George Kuhn, former Director of Research Services at
Research & Marketing Strategies, Inc. (RMS); https://rmsbunkerblog.wordpress.com/2012/04/23/13-
traits-of-a-good-data-analyst-market-research-careers/
14
The charts above contain sensitive information that is protected and privileged and must not be

11. Abbreviations
Table 3 – Abbreviation Table
Acronym Description
ASAI average service availability index
CAIDI customer average interruption duration index
CIII customers interrupted per interruption index
CLEI common language equipment identifier
CLLI common language location identifier
CMI customer minutes of interruption
CPUC California Public Utility Commission
CR customer relationship
DOE Department of Energy
FCC Federal Communications Commission
IEEE Institute of Electrical and Electronics Engineers
ISBE International Society of Broadband Experts
MED(s) major event day(s)
MTRS mean time to restore service
MTTX mean time to x with “X” signifying a variable, i.e., MTTR (Mean Time to
Restore), MTTA (Mean Time to Acknowledge), etc.
NARUC National Association of Regulatory Utility Commissioners
NERC North American Electric Reliability Corp.
NOC network operations center
NORS (FCC) network outage reporting system
PCN product change notice
PUC Public Utility Commission
RMS Research & Marketing Strategies, Inc.
SAIDI system average interruption duration index
SAIFI system average interruption frequency index
SCTE Society of Cable Telecommunications Engineers
TCPA Telephone Consumer Protection Act
TWC Time Warner Cable

Trading with Utilities
Cable Facility and Customer Opportunities
A Technical Paper prepared for SCTE/ISBE by
Robert F. Cruickshank III, RA, University of Colorado at Boulder, SCTE/ISBE Member
Visiting Researcher, National Renewable Energy Laboratory
Consultant, Cable Television Laboratories, Inc.
rfciii@cruickshank.org
+1-703-568-8379
Laurie F. Asperas-Valayer, Strategy Consultant, Le Savoir Faire
asperasvalayer@gmail.com
+1-631-335-9197
Dr. Ronald J. Rizzuto, University of Denver, SCTE/ISBE Member
J. William Sorensen Distinguished Professor in Finance
Endowed Chair, Executive Education, Reiman School of Finance
Ronald.Rizzuto@du.edu
+1-303-871-2010

Table of Contents
Title Page Number
Table of Contents ___________________________________________________________________ 22
1. Abstract ______________________________________________________________________ 23
2. Introduction ___________________________________________________________________ 23
3. Real Time kWh Pricing & The Evolution of Net Metering ________________________________ 24
3.1. “Day Ahead Pricing" and "Real Time Pricing" __________________________________ 25
3.2. Net Energy Metering _____________________________________________________ 27
3.3. Net Energy Metering Evolving to Feed-In Tarriffs _______________________________ 27
4. Financial Opportunities created by RTP and Feed-In Tarrifs _____________________________ 28
5. Cable Facility Opportunities ______________________________________________________ 28
6. Cable Customer Opportunities ____________________________________________________ 29
7. Customer Experience Management & Lifetime Value __________________________________ 29
8. Summary_____________________________________________________________________ 29
9. Abbreviations and Definitions _____________________________________________________ 30
9.1. Abbreviations ___________________________________________________________ 30
9.2. Definitions______________________________________________________________ 30
10. Bibliography___________________________________________________________________ 31
List of Figures
Title Page Number
Figure 1 - Maximizing Use of Renewables over 2-days 24
Figure 2 - Pricing Orchestrates Demand and Maximizes Use of Renewables Over 7-days 25
Figure 3 - Wholesale Market Day Ahead Pricing 26
Figure 4 - Wholesale Market Real Time Pricing 26
Figure 5 - Conventional Net Energy Metering 27

1. Abstract
Electricity grids all over the world are increasingly facing a future for which they were not designed. In
the rapidly evolving renewable energy and storage landscape, massive amounts of variable solar, wind
and hydroelectric “distributed generation” are being connected to what still is primarily a one-way
distribution grid. Just as the Cable TV infrastructure was adapted to provide two-way interactive and
Internet services, so too, electricity grids will evolve.
To accommodate and maximize the variable nature of renewable energy sources, new interconnect
mechanisms and algorithms will be used pervasively around the world to orchestrate consumer demand
minute-by-minute throughout the day in order to meet supply. Real Time Pricing will be broadcast over
the Internet1
to commercial and residential appliances to encourage refrigerators, hot water heaters and
electric vehicle chargers to maximize demand when there is an abundance of renewable or conventional
generation capacity. Alternatively, during hours of electricity scarcity, appliances will minimize demand
by delaying their respective jobs. At the core of this orchestration will be logic that makes or breaks
business models for customers to “buy low and sell high” while producing and consuming electric power.
Household appliances and even batteries will provide energy elasticity enabling per-building
independence, and will play an increasingly important role, e.g., Elon Musk’s “Master Plan Part Deux”.
As electricity grids transition to two-way, there will be many parallels to cable industry developments and
deployments over the last 25 years. Examples include fair bandwidth distribution algorithms and other
problems solved in the development and refinement of the Data Over Cable Service Interface
Specifications a.k.a. DOCSIS®2
.
2. Introduction
In this paper we will discuss first steps on the path to the Cable Industry’s success in having a lucrative
piece of the energy pie. Two market forces are opening new realms of possibility: 1) Real Time Pricing,
and 2) the reinvention of Net Metering known as Feed-in Tariffs.
Electricity costs money and there’s money to be made. The worth of a watt is rapidly changing. In recent
years, energy production, management, sales and use have been steadily metamorphosing. The explosion
of connected home energy management systems, private renewable energy production and new energy
retail schemes, presents real opportunity for the global cable industry.
Indeed, the global cable industry has an amazing shot at swooping in to take advantage of evolution in
energy production and retail sale. This, combined with new technologies in energy efficiency and the
connected home will allow cable to really cash-in using pricing schemes through new electrical energy
(watt-hour) metering. Cable companies will benefit from this new paradigm resulting in reduced
operations costs; cable customers will benefit from energy savings and will be incented to stay on as
subscribers, thereby reducing dreaded attrition.
1
Internet in the broadest sense, including but not limited to: Smart Meters, Power Line Carrier (PLC) networks, In-
home networks, Wi-Fi, LTE, 5G, Data over Cable Service Interface Specifications, etc.
2
DOCSIS is a trademark of CableLabs

3. Real Time kWh Pricing & The Evolution of Net Metering
It is critical that energy is converted, generated and distributed as efficiently as possible, liberating no
more than absolutely necessary while optimizing use of new and conventional power plants,
transmission/distribution systems and clean renewable resources. Orchestrating electrical demand to meet
supply will help minimize energy generation and “spinning reserve” inefficiencies, maximize use of
renewables, and reduce thermal and greenhouse gas pollution.
In homes and businesses, real time kilowatt-hour (kWh) pricing will encourage:
• “off-peak” pre-cooling of buildings, battery charging, and
• “on-peak” load shedding, diesel electric generation3
and battery discharging.
Pricing schemes will be used in home and business micro grids to sculpt/orchestrate demand to meet
supply as shown in Figure 1 and Figure 2 where dramatic efficiency improvements are complimented by
renewables and storage over 2-day and 7-day periods45
.
Figure 1 - Maximizing Use of Renewables over 2-days
3
Diesel electric backup power may have a limited future; there is talk of laws to prevent diesel and other polluting
generation for this purpose
4
http://www.rmi.org/RFGraph-hourly_high_penetration_renewables
5
http://www.rmi.org/RFGraph-hourly_operability_on_microgrid

Figure 2 - Pricing Orchestrates Demand and Maximizes Use of Renewables Over 7-days
3.1. “Day Ahead Pricing" and "Real Time Pricing"
Day ahead pricing (DAP) and real time pricing (RTP) are electric supply options in which customers pay
electricity rates that vary by hour. In addition to fixed-priced electric supply rates common today, utilities
will increasingly offer electricity purchase rate plans that charge time-varying pricing reflective of the
costs of electric supply. Unlike utilities' fixed-priced electric supply rates, utilities' charge customers for
the electricity they consume each hour based on the corresponding hourly market price of electricity.
With DAP programs, hourly prices for the next day are set the night before and can be communicated to

customers so they (most likely their automated smart home systems) can determine the best time of day to
use major appliances. Figure 3 depicts DAP at a wholesale market level6
.
Figure 3 - Wholesale Market Day Ahead Pricing
With RTP programs, prices are based on the actual real-time hourly market price of electricity during the
day and customers are notified when real-time prices are high or are expected to be high7
so they can
respond in real-time and shift the use of major appliances to lower priced hours. While savings are not
guaranteed, customers can manage electricity costs under RTP by shifting use of electricity from hours
when prices are higher to hours when prices are lower. To participate in a residential RTP, customers
without a smart meter must have a meter installed that is capable of recording hourly usage. Figure 4
depicts RTP at a wholesale market level8
.
Figure 4 - Wholesale Market Real Time Pricing
6
http://www.eia.gov/todayinenergy/images/2011.09.26/FERCday-ahead.png
7
In some states customers will want to be notified when their demand is setting new monthly high. In Arizona a
mandatory component of all solar customers’ bills now includes a capacity charge based on maximum demand.
8
http://www.eia.gov/todayinenergy/images/2011.09.26/FERCreal-time.png

3.2. Net Energy Metering
Today’s outdated net energy metering (NEM) allows consumers who generate electricity to use the grid
as a storage battery and then later use electricity anytime (instead of just when it is generated). This is
particularly important with wind and solar, which are non-dispatchable9
. Monthly NEM allows
consumers to use solar power generated during the day at night, or wind from a windy day later in the
month. Annual NEM rolls over a net kilowatt credit to the following month, allowing solar power that
was generated in July to be used in December, or wind power from March to be used in August.
NEM policies can vary significantly by country and by state or province: a) if NEM is available, b) if and
how long you can keep your banked credits, and c) how much are the credits worth (retail/wholesale).
Most NEM laws involve monthly roll over of kWh credits, a small monthly connection fee, require
monthly payment of deficits (i.e. normal electric bill), and annual settlement of any residual credit.
Unlike a feed-in tariff (FIT), which requires two meters, NEM uses a single, bi-directional meter and can
measure current flowing in both directions (to and from the utility). NEM can be implemented solely as
an accounting procedure, and requires no special metering, or even any prior arrangement or notification.
Figure 5 shows Conventional Net Energy Metering10
.
Figure 5 - Conventional Net Energy Metering
3.3. Net Energy Metering Evolving to Feed-In Tarriffs
NEM has been used as an enabling policy designed to foster private investment in, and high penetration
of, renewable energy. Successful at first, NEM policy is presently failing because people who are not
generating are paying the “freight” for those that are generating electricity. For example, apartment
dwellers, those who live in shady areas and those who can’t afford a solar system, all end up supporting
the shipping costs for those that are generating electricity. For a case in point, in Newsday’s recent article
“PSEG Raises Power Supply Charge Again”, consumers are experiencing a 35% price hike over the last 6
months11
. This phenomenon occurs as more and more residences and businesses sign-up with subsidized
solar panels using NEM interconnection.
9
Non-dispatchable means generation that is weather dependent and cannot be controlled.
10
http://cdn2.hubspot.net/hub/398098/file-1115094088-jpg/blog-files/solar-net-metering.jpg
11
http://www.newsday.com/long-island/pseg-power-supply-charge-rises-again-1.12017656

The rescinding and decline of NEM tariffs will dramatically change the worth of a watt: electricity will be
purchased by consumers, for example, at $0.20/kWh, yet consumers will only be able to sell back the
energy they produce at $0.10/kWh.
4. Financial Opportunities created by RTP and Feed-In Tarrifs
The combination of real-time pricing and feed in tariffs create a vast new realm of opportunities in the
purchase and sale of electricity. Through the implementation of creative building management
algorithms, businesses and consumers will be able to “buy low” and “sell high” hour-to-hour or even
minute-to-minute as part of normal daily operations.
While the rescinding of net metering may slow penetration of distributed generation via renewable
resources, the arrival of RTP and feed-in tariffs will firmly establish the concept of micro grids on a per-
building basis. In addition, the concept of “Islanding”, where a building can survive on its own without
the grid, will also be accelerated as control systems are deployed to not only save consumers’ money and
improve efficiency of building operations, but to also allow buildings to operate completely
independently of the grid when necessary, for example during hurricanes and other outages. Data centers
will lead this charge, along with hospitals, military and law enforcement.
Though financial projections and scenarios are beyond the scope of this paper, suffice it to say that in the
area of micro grids and building controls, there will be innovation and development for decades to come.
There will be countless opportunities for the cable industry to protect the environment and save
operations expense while providing energy management services that help customers save money.
5. Cable Facility Opportunities
As stated previously, real time kilowatt-hour (kWh) pricing will encourage Cable TV data
center/headend/hub: a) off-peak pre-cooling and battery charging, and b) on-peak load shedding, diesel
electric generation and battery discharging.
The first step in Cable facility planning is to task a subject matter expert with identifying and estimating
financial savings opportunities created by real-time pricing. The next step is to consider how and when to
use feed-in tariffs to sell energy back to the utility.
Many cable facilities have local electricity storage in the form of uninterruptible power supplies that
provide battery-backed power during outages. In addition, Cable facilities may have distributed
generation such as diesel generators, solar panels, etc.
Weather forecasts will become increasingly important in the prediction of the financial costs and rewards,
and the amount of energy that:
• Will be required for heating and cooling of a facility
• Will be produced by solar panels
Throughout the day, it will make sense to buy energy at the lowest prices and to sell energy at highest
prices. Updated weather forecasts will allow in-building control systems to fine-tune predictions for
when electricity should be bought and sold. During times of extremely high prices (e.g., during grid
emergencies, natural disasters and outages), it may make sense to sell diesel electric power or to partially

discharge uninterruptible power supply batteries by providing power to the utility grid for short periods of
time where it makes financial sense to do so.
6. Cable Customer Opportunities
In support of Industry commitment to energy efficiency, the above scenario can and should be applied to
all entities that sell or consume energy. This includes our residential and business customers who will
need new cable-provided services and infrastructure that receives pricing signals, weather forecasts, and
then advises throughout the day when to “buy and sell” in order to maximize use of renewable energy
thus minimizing electricity costs.
During times of disaster, a very important opportunity for cable customers is access to energy. This
includes individual homes and humanitarian efforts such as crisis centers and hospitals. Each residence
and business micro grid that is able to “Island” itself and even offer energy to neighbors becomes a
distributed grid asset.
7. Customer Experience Management & Lifetime Value
As the global cable industry moves forward with this opportunity, communication between customers and
industry professionals will be a priceless resource. Globally pooling information and feedback through
customer experience management will be essential in beating the competition (FiOS, etc.) in building a
successful integrative system that serves both industry and customer interest. Cable’s high penetration of
customer relationships gives it a first mover’s advantage in home automation, though competition from
incumbent utilities, and the likes of Amazon, Apple, and Google is close behind.
Focusing on the customer experience before this new RTP service is launched will enable the MSO to
develop an end-to-end, fully integrated experience for the customer. By utilizing and putting in place
from the beginning of the customer journey some of the CEM ‘lessons learned’ like 1) the importance of
‘onboarding’, 2) the criticality of the ‘first 90 days’, 3) ‘one stop’ solutions to customer problems and, 4)
being a business that is ‘easy to do business with’, will insure ongoing industry success and lifetime value
of a customer. CEM investments are not only the “right thing” to do, but also enhance the bottom line.
8. Summary
There is no doubt that evolution in energy management is necessary for supporting a sustainable world.
The cable industry has real opportunity in taking on the challenges discussed in this paper. In doing so
the cable industry not only increases opportunity for its own financial gain, it also supports the goals of
SCTE 2020 for efficient energy management by the end of the decade and takes action for sustainable
energy systems. This may be regarded as a stand for our planet Earth where we all win.
Taking the first step in designing and implementing viable standards in pricing and metering systems for
facilities and for consumers will be the flagship into a new paradigm, where the cable industry is no
longer the energy glutton but the energy economist. Our best engineers should work hand-in-hand with
utilities and public service commissions in designing necessary standards. As we go into this new realm
of possibility we support both industry and consumer needs through application of energy management
services and continuous customer experience management. In this way both industry professionals and
consumers are active partners in reducing the carbon footprint. We all become part of the solution to a
very old and dirty problem.

9. Abbreviations and Definitions
9.1. Abbreviations
CHP combined heat and power
DAP day ahead pricing
DER distributed energy resources
DERMS distributed energy resource management system
DES distributed energy storage
DG distributed generation
DOCSIS Data Over Cable Service Interface Specifications
kWh kilowatt hour
NEM net energy metering
PV photovoltaic solar power
RTP real time pricing
9.2. Definitions
Day Ahead Pricing Day Ahead Pricing (DAP) is generally an hourly rate where prices for
the next day are set the night before.
Feed-in Tariff A Feed-in Tariff is an economic policy created to promote active
investment in and production of renewable energy sources. Unlike the
value equality inherent in NEM, Feed-in Tariffs generally value
energy purchased from consumers at a lower value than energy sold to
consumers.
Kilowatt hour The basic billing unit of electricity equivalent to drawing 1000 Watts
for one hour.
Micro grid A micro grid is a group of interconnected loads and distributed energy
resources within clearly defined electrical boundaries that acts as a
single, controllable entity with respect to the grid. Micro grids
typically can include a mix of conventional and renewable generation
resources, as well as energy storage.
Net Energy Metering Net Metering is system in which solar panels or other renewable
energy generators are connected to a public-utility power grid and
surplus power is transferred onto the grid, allowing customers to offset
the cost of power drawn from the utility wherein the sale and purchase
prices are equal. Initially used to stimulate market penetration, NEM
has been rescinded in many areas in favor of a lower value feed-in
tariff.
Real Time Pricing Real-time pricing (RTP) is generally an hourly rate which is applied to
usage on an hourly basis.

10. Bibliography
[1] K McKenna and A Keane, “Electrical and Thermal Characteristics of Household Appliances:
Voltage Dependency, Harmonics and Thermal RC Parameters,” 2016.
[2] K. McKenna and A. Keane, “Residential Load Modeling of Price-Based Demand Response for
Network Impact Studies,” IEEE Transactions Smart Grid, vol. 7, no. 5, pp. 2285–2294, 2016.
[3] K. McKenna and A. Keane, “Discrete elastic residential load response under variable pricing
schemes,” pp. 1–6, 2014.
[4] R. F. Cruickshank III, “Orchestrating Utility Supply and Demand in Real-time via the Internet,
Home Networks, and Smart Appliances,” Proceedings of the IASTED Conference on Power and
Energy Systems - 2008, vol. 617, no. 089, p. 6, Apr. 2008.
[5] NEMA, “Powering Microgrids for the 21st-Century Electrical System,” The National Electrical
Manufacturers Association, NEMA, MGRD 1-2016, Jan. 2016.
[6] S. Walsh, “Amory B. Lovins, the Rocky Mountain Institute, Reinventing Fire: Bold Business
Solutions for the New Energy Era,” Technological Forecasting and Social Change, vol. 80, no. 3,
pp. 556–558, 2013.
[7] R. F. Cruickshank III, “Optimal Real-time Distributed Control of Electric Power Generation and
Utilization via a Ubiquitous Data Communications Infrastructure,” University of Colorado, Boulder,
CO USA, 1996.
[8] R. F. Cruickshank III, “Cable Energy Management – A New Horizon in the Digital Home,” Society
of Cable Telecommunications Engineers, Digital Home Symposium, Orlando, USA, October 2012
[9] Katherine Tweed ,”Internet and Cable Giant Comcast Will Soon Sell Electricity in Pennsylvania”,
Greentech Media, January 23, 2014, http://www.greentechmedia.com/articles/read/comcast-will-
sell-electricity-in-pennsylvania
[10] Mark Brinda, “Connected Homes: What Role Should Energy Companies Play?”, Bain & Company,
April 23, 2014, http://www.bain.com/publications/articles/connected-homes.aspx
[11] Wikipedia, “Price per watt”, https://en.wikipedia.org/wiki/Price_per_watt
[12] Electricities.com, “History of Metering”,
http://www.electricities.com/Libraries/2012_Meter_Safety_Workshop/History_of_Metering_2012.sf
lb.ashx

How Power Purchase Agreements Impact Your
Energy Strategy
Understanding Financial Consideration and Risks of Off-Site
Alternative Energy Procurement Strategies
John W. duPont, P.E., Principal of Distributed Energy Resources, EnerNOC, Inc.
SCTE/ISBE Member
One Marina Park Drive, Suite 400
Boston, MA 02210
jdupont@enernoc.com
(617)535-7344
Alvaro E. Pereira, PhD, Director of Supply Advisory Services, EnerNOC, Inc.
SCTE/ISBE Member
100 Front Street, 20th
Floor
Worcester, MA 01608
Alvaro.pereira@enernoc.com
(508)459-8118

Table of Contents
Title Page Number
1. Introduction ___________________________________________________________________ 35
2. Evaluating Power Purchase Agreements for Alternative Energy __________________________ 35
2.1. Off-Site Power Purchase Agreements ________________________________________ 36
2.1.1. Definition ______________________________________________________ 36
2.1.2. Financial Consideration of a Physical PPA ____________________________ 37
2.1.3. Direct Financial Consideration of a Virtual PPA ________________________ 38
2.1.4. Net Energy Budget Considerations of a VPPA _________________________ 41
2.1.5. Consideration of Cross-Market Risk of a VPPA ________________________ 44
2.1.6. Consideration of “Shape Risk” and Future Price Changes on a VPPA_______ 45
3. Conclusions___________________________________________________________________ 46
4.1. Abbreviations ___________________________________________________________ 47
4.2. Definitions______________________________________________________________ 47
List of Figures
Title Page Number
Figure 1 - Virtual Power Purchase Agreement Structure 36
Figure 2 - Retail Electricity Price Projections based on Oil and Gas Price and Resource Availability
Assumptions and Varied Economic Growth Assumptions, from US Energy Information
Administration, 2015 Annual Energy Outlook 37
Figure 3 - ERCOT North Hub Wholesale Price Projections based on Varied Heat Rate and Scenarios
from Energy Information Administration’s 2015 Annual Energy Outlook 38
Figure 4 - ERCOT North Hub Real-Time Market Average Hourly Prices ($/MWh), 2015 39
Figure 5 - Average Hourly Profiles in January and June for a representative customer load, wind facility
production, solar facility production, and real-time market price. 40
Figure 6 - Output of Hourly Analysis Comparing Modeled Wind Production against ERCOT North Real-
Time Market Prices in 2015 41
Figure 7 - Budget Impact of VPPA with Indexed Electricity Purchasing 42
Figure 8 - Summary of Estimated Calendar Year Fixed Energy Supply Contract Rate, based on On-
Peak and Off-Peak Monthly Forward Prices Observed Three Months Prior. Source: ICE via
Morningstar 43
Figure 9 - Net Energy Spend for Different Energy Supply Purchasing Strategies Combined with a
Virtual Power Purchase Agreement 44
Figure 10 - Net Energy Spend after Adding the Same VPPA Gain/Loss, with Loads Centered in
Different Grid Regions 45
Figure 11 - Day-Ahead Average Hourly Electricity Prices at SP-15 (CAISO), May 2012 and May 2016.
Source: SparkLibrary, based on data from CAISO 46

List of Tables
Title Page Number
Table 1 - Summary of Market Risk Impacts to Virtual Power Purchase Agreement 46

1. Introduction
As a key initiative of the Energy 2020 program, the Society of Cable Telecommunications Engineers
(SCTE) is committed to developing new standards and operational practices that establish methods to
define and continuously analyze what existing and alternate energy sources are best to supply the various
components of a cable service network.
This technical paper examines procurement of alternative energy or associated credits from generating
assets located off-site, through power purchase agreements, or bilateral contracts with renewable energy
asset owners or utilities for the electricity or environmental attributes associated with electricity produced
by such generating facilities, and discusses the benefits and the risks associated with such purchases in the
context of the buyer’s whole portfolio. This paper will consider how power purchase agreements impact
several of the objectives of SCTE’s Alternative Energy working group, including providing an analysis of
financial impacts concerning such investments, and assessing the ability to reduce carbon footprint of
cable operations through alternative energy sources and reduced dependency on grid energy.
2. Evaluating Power Purchase Agreements for Alternative Energy
As many organizations, including SCTE members, set goals to reduce carbon intensity and increase the
percentage of their operations that are powered by alternative energy sources, procurement and
sustainability stakeholders are increasingly turning to off-site alternative energy procurement as a way to
make meaningful progress toward achieving their goals. In fact, 80% of respondents indicated that their
organization intended to purchase off-site alternative energy in PwC’s June 2016 Corporate Renewable
Energy Purchasing Survey Insights, compared with just 57% who had made such purchases in the past.
Procurement of electricity from off-site alternative generation sources like wind, solar, and geothermal is
typically financed by a third party through a power purchase agreement (PPA), wherein the end-use
customer agrees to pay for electricity generated by the asset. These payments generate a cash flow that
enables counterparty, typically a renewable energy developer, to raise financing from equity investors in
order to build the generating asset. While other options for investment and outright ownership of off-site
generating assets exist, PwC’s survey indicates that the majority of corporations see power purchase
agreements as their primary path to securing alternative energy.
Power purchase agreements have several potential benefits to buyers, including the ability to secure
electricity generation from alternative sources without contributing capital up-front, the potential to
reduce the cost of grid-purchased electricity, and the ability to mitigate exposure to long-term electricity
price volatility.
For organizations that need to demonstrate that their investments create additional alternative energy
generation on the grid, PPAs can provide a way for sustainability teams to make such claims. By
promising a fixed rate of return for new wind and solar projects which would otherwise not get financed
and built, as corporate alternative energy procurement pioneers like Google have stated, their position as a
counterparty in the PPA enables such projects to receive equity and debt financing to facilitate
construction, thus adding to the installed base of renewable energy on the grid.

2.1. Off-Site Power Purchase Agreements
2.1.1. Definition
Power purchase agreements fall under two general structures: physical and synthetic, or virtual. Under a
physical PPA, the seller counterparty owns and operates the generating asset, sells the energy generated to
one or more customers, and may sell the renewable energy credits (RECs) associated with such
generation as well. The seller can be responsible for transmission of the power to a liquid electricity
market, but this is not common. This arrangement is feasible only in states where end-use customers are
allowed to contract with third-parties other than the utility for the supply of energy, and requires the buyer
to take title to the energy as well as the responsibility for scheduling and all market interactions, either
directly with the independent system operator (ISO) or through a state-licensed retail energy supplier.
Because managing such transactions requires specialized knowledge of electricity market rules, many
end-use customers have opted to pursue the second structure, virtual PPAs.
In a virtual power purchase agreement (VPPA), the company developing the renewable project sells the
power to the grid when the project is complete. In order to get equity and debt financing for the project,
the developer enters into a VPPA with a counterparty buyer who agrees to pay a certain price for
electricity generated by the asset, just like in the physical PPA case. In contrast to the previous case,
however, the buyer counterparty in a VPPA arrangement does not take title to the electricity generated,
and is not responsible for any market transactions to sell the power. Instead, the seller counterparty
(typically the developer), takes on this responsibility and agrees to sell the power to an agreed point in the
local electricity market, at the floating market price of power, typically settled on an hourly or more
frequent basis. Figure 1 below displays a typical structure for a virtual power purchase agreement where
the buyer takes possession of the RECs associated with the project.
Figure 1 - Virtual Power Purchase Agreement Structure
Buyer purchases electricity
under separate transactions
Renewable Energy Project
Company(“Seller”)
Virtual PPA Counterparty
(“Buyer”)
Real-Time or Day-Ahead
Electricity Market
Electricity Renewable
Energy
Certificates
(RECs)
$ $

If the electricity sells on the grid for less than the agreed amount, the buyer of the VPPA will pay the
difference to the seller; if the electricity sells to the grid for more than the fixed price, the buyer of the
VPPA will collect the difference from the seller. In this arrangement, there are a few benefits for all
parties: The developer of the solar array or wind farm has the price security it needs to get financing for
the project, and the buyer is able to make the claim (by acquiring the RECs associated with the
generation) that their investment has contributed to “additional” alternative energy generation.
In addition, the financial arrangement of a VPPA can provide some protection against fluctuations in
wholesale electricity rates, because the structure pays the buyer more if wholesale energy prices rise.
Even after entering into a VPPA, a buyer must still purchase energy to physically power facilities, and the
prices the buyer pays for retail power will be influenced by the same drivers of wholesale rates.
Depending on the methods chosen for purchasing physical power for the facilities, holding a long-term
position in a VPPA can improve budget certainty around a historically volatile cost line item. This
concept will be further explored in Section 2.1.5 below, along with the associated market risks imposed
by a VPPA purchase.
2.1.2. Financial Consideration of a Physical PPA
For a corporation seeking to buy renewable energy, the financial analysis of a physical power purchase
agreement typically centers on the price agreed upon by the buyer and seller. Assuming that the buyer
will take physical delivery of the power, and thus replace their current electricity supply quantity with that
of the PPA, the buyer should consider the PPA price against their best estimates of market prices for the
duration of the contract. Because most contracts have durations exceeding 10 years, however, buyers need
to evaluate the potential outcomes of technological and regulatory impacts to electricity prices. As shown
in Figure 2 below, the US Energy Information Administration projected that retail electricity prices could
stay flat from 2013 benchmarks or increase by nearly 20% over the lifespan of a theoretical 20-year PPA.
Figure 2 - Retail Electricity Price Projections based on Oil and Gas Price and Resource
Availability Assumptions and Varied Economic Growth Assumptions, from US Energy
Information Administration, 2015 Annual Energy Outlook

A buyer should consider different market outcomes to understand the expected minimum and maximum
return on investment for a PPA. For example, using scenarios detailed in the US Energy Information
Administration’s 2015 Annual Energy Outlook along with other forecasts and forward market conditions,
coupled with varying assumptions about the average heat rate of natural gas power plants on the grid in
Texas’s electricity market run by Energy Reliability Council of Texas (ERCOT), EnerNOC estimated that
wholesale prices in ERCOT North Hub could vary from $30.84/MWh to $67.6/MWh by 2026, halfway
through a 20-year PPA term. Each buyer will have a different attitude toward the risk associated with
such changes in market price, and should take care to understand the range of possible outcomes for any
large purchase.
Figure 3 - ERCOT North Hub Wholesale Price Projections based on Varied Heat Rate and
Scenarios from Energy Information Administration’s 2015 Annual Energy Outlook
2.1.3. Direct Financial Consideration of a Virtual PPA
The financial analysis of a virtual power purchase agreement adds another dimension of complexity on
top of future average price scenario analysis, because the VPPA introduces exposure to hourly market
prices. In the terms of the agreement, the buyer and seller must first agree to the point at which the seller
will deliver the power, which governs the price at which the contract is settled. The counterparties must
also agree to the market index (i.e., whether real-time or day-ahead) that will be used to sell the power
and settle the contract.

In major competitive electricity markets, like ERCOT and the PJM Interconnection in the Mid-Atlantic,
wholesale market participants can buy and sell electricity at different locations in the market. While most
transactions are focused around liquid market hubs, such as ERCOT North and PJM West, customer
demand for power must be physically met at different geographic points in the system, typically referred
to as “nodes.” While the price at the market hub is essentially based on an uncongested delivery system
for power, the locational marginal price (LMP) at the node is based on both the market hub price as well
as the cost of transmission constraints that require more expensive power to be used due to inability of
delivery infrastructure.
In order to understand the financial valuation of a VPPA, the buyer should consider the hours at which
power will be generated by the asset, because prices can vary significantly hour-by-hour within the same
index market, and hourly pricing profiles adjust season-by-season as well. As Figure 4 below shows,
ERCOT North Hub real-time market prices varied dramatically across 2015, with highest pricing during
August afternoon hours and lowest pricing during December overnight hours. For a customer considering
a VPPA contract with a “strike price” (or agreed fixed price) of $25/MWh, the VPPA would add cost to
the buyer’s energy expense whenever the market price is below $25/MWh, and reduce overall purchasing
costs when the market price exceeded $25/MWh.
Figure 4 - ERCOT North Hub Real-Time Market Average Hourly Prices ($/MWh), 2015
Still, the difference in market prices is only one factor in the economic viability of a VPPA, since the
quantity delivered by hour also changes. As Figure 5 shows, the average hourly generation for a wind
facility and an equivalent solar facility, each delivering an equal amount of energy on an annual basis, can
vary significantly month by month.
These curves, and the subsequent analysis, are based on the following assumptions:
• The customer load profile is based on an anonymized manufacturing location based in North
Texas, with annual energy consumption of 4,680,000 kWh
• The wind production profile is based on a proprietary model for an actual facility located in North
Texas, scaled linearly to match the annual output of 4,680,000 kWh. The modeled wind facility
has a net capacity factor of 45%, and a modeled nameplate capacity of 1.26 MW.
• The solar production profile is based on a simulation run on PVWatts® Calculator, for a solar
facility based in Pflugerville, Texas, where 2-axis tracking modules are tilted 30 degrees and face
due South. The hourly production output is scaled to match the annual output of 4,680,000. The
Hour
Ending:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
January 20 20 19 20 21 23 36 25 25 27 25 23 22 22 21 21 22 28 26 25 25 23 22 21
February 17 17 17 17 19 21 34 25 41 51 39 24 22 22 21 21 21 24 44 25 31 26 21 18
March 22 23 21 17 22 24 41 25 25 33 32 28 28 32 30 28 28 30 39 40 28 24 23 21
April 23 17 16 16 18 20 20 20 21 22 23 23 24 27 28 34 36 33 23 22 30 21 20 21
May 19 16 14 14 16 17 19 19 20 21 23 25 30 36 50 46 35 29 25 24 28 24 24 24
June 20 17 16 16 17 18 17 19 20 21 26 25 27 28 29 31 31 28 25 24 24 23 22 21
July 20 19 17 16 17 17 18 19 21 22 25 28 31 33 40 56 51 38 33 28 27 25 24 21
August 20 18 17 16 16 18 19 19 20 22 27 28 33 39 66 114 98 48 31 29 28 25 22 21
September 18 16 16 15 16 17 18 17 18 20 21 24 28 33 33 41 43 30 27 25 24 22 20 19
October 17 15 14 13 14 18 24 17 18 20 20 19 21 22 24 29 26 24 23 23 20 18 20 17
November 19 14 13 13 13 15 23 18 19 19 20 19 19 20 19 19 21 36 22 22 19 18 17 15
December 12 11 11 11 11 13 18 17 17 17 17 16 15 15 14 15 16 28 19 17 17 15 15 13
ERCOT North Hub - 2015 Average Hourly Prices ($/MWh)

modeled solar facility has a capacity factor of 22%, and a modeled nameplate capacity of 2.39
MW.
• Hourly prices are averaged based on 15-minute real-time market prices for ERCOT North Hub
Figure 5 - Average Hourly Profiles in January and June for a representative customer
load, wind facility production, solar facility production, and real-time market price.
Based on the modeled output, this wind facility generates less power during summer afternoon hours,
when market prices are more favorable to the buyer, and generates more power during the winter evening
and overnight hours, when market prices are more favorable to the seller. In order to fully comprehend
the financial performance of the VPPA, then, the prospective buyer must consider an hourly analysis of
expected production against the hourly average market price. This analysis summarized in Figure 6
$0
$20
$40
$60
$80
$100
$120
$140
$160
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
MarketPrice($/MWh)
Load,Generation(MWh)
Average Hourly Load and Generation, January 2015
Load
100% Wind
100% Solar
ERCOT North Hub Price
$0
$20
$40
$60
$80
$100
$120
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
MarketPrice($/MWh)
Load,Generation(MWh)
Average Hourly Load and Generation, July 2015
Load
100% Wind
100% Solar
ERCOT North Hub Price

Figure 6 - Output of Hourly Analysis Comparing Modeled Wind Production against
ERCOT North Real-Time Market Prices in 2015
While the hourly analysis represented in Figure 6 provides a summary of the financial performance of the
VPPA itself against historical market conditions, a prospective buyer must finally consider the method in
which their facilities purchase electricity to power their operations, because the VPPA does not provide
any physical delivery of the power generated by the asset.
2.1.4. Net Energy Budget Considerations of a VPPA
To understand how different electricity purchasing methods align with the financial performance of a
VPPA, a prospective buyer should assess the potential impact to their energy budget of the VPPA
combined with a conventional purchasing strategy. Because hourly prices depend on many factors such as
availability of generating assets across the grid, input commodity prices, and transmission constraints, it is
difficult to forecast hourly prices. Instead, we consider how the combined VPPA and purchasing strategy
would have fared in different recent market scenarios, to provide a range of possible outcomes.
In the simplest case, we consider the total budget impact if the facility were buying 100% of its power
from the same electricity spot market as used as the floating price in the VPPA against a fixed strike price
of $25/MWh. Note that this analysis and all subsequent analyses include only the wholesale energy
component of the buyer’s energy budget, and ignores capacity, ancillaries, transmission, and local utility
distribution charges.
Hour Ending: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Total
January -$87 -$90 -$94 -$86 -$84 -$49 $362 $20 $11 $80 $25 -$23 -$30 -$40 -$57 -$48 -$28 $107 $39 $4 $18 -$37 -$55 -$80 -$219
February -$121 -$137 -$143 -$110 -$114 -$67 $149 -$17 $73 $290 $293 -$5 -$28 -$37 -$58 -$66 -$61 -$31 $208 -$15 -$4 -$44 -$77 -$120 -$241
March -$46 $19 -$6 -$133 $15 $46 $295 $16 $6 $108 $83 $47 $73 $143 $105 $16 $16 $75 $468 $468 $94 -$14 -$39 -$90 $1,764
April $15 -$134 -$166 -$175 -$166 -$115 -$103 -$101 -$71 -$60 -$39 -$41 -$26 $16 $50 $85 $217 $242 -$43 -$62 $21 -$87 -$109 -$82 -$935
May -$143 -$221 -$262 -$237 -$203 -$179 -$132 -$104 -$83 -$74 -$43 $38 $168 $321 $570 $573 $214 $86 $2 -$15 $3 -$37 $1 -$8 $235
June -$110 -$163 -$179 -$177 -$156 -$119 -$118 -$84 -$56 -$43 $5 -$7 $29 $35 $52 $72 $79 $41 $10 -$11 -$22 -$48 -$64 -$94 -$1,126
July -$89 -$122 -$137 -$144 -$131 -$101 -$76 -$68 -$35 -$21 -$4 $13 $39 $52 $95 $194 $206 $114 $93 $63 $41 $14 -$19 -$63 -$86
August -$91 -$104 -$114 -$112 -$100 -$78 -$61 -$55 -$36 -$14 $47 $24 $57 $97 $244 $528 $477 $205 $98 $78 $70 $3 -$41 -$77 $1,044
September -$120 -$147 -$137 -$134 -$130 -$123 -$82 -$86 -$53 -$33 -$18 -$5 $27 $34 $59 $97 $129 $37 $24 -$1 -$6 -$43 -$79 -$106 -$897
October -$147 -$167 -$170 -$168 -$143 -$47 $68 -$91 -$80 -$58 -$57 -$63 -$44 -$29 -$6 $8 $19 -$10 -$30 -$38 -$92 -$113 -$56 -$143 -$1,654
November -$169 -$249 -$252 -$247 -$223 -$173 $56 -$81 -$76 -$73 -$55 -$69 -$68 -$61 -$75 -$75 -$75 $82 -$69 -$51 -$131 -$142 -$165 -$205 -$2,646
December -$270 -$256 -$269 -$294 -$293 -$239 -$86 -$134 -$126 -$118 -$110 -$121 -$144 -$162 -$168 -$137 -$101 $2 -$97 -$148 -$155 -$198 -$222 -$267 -$4,112
Total -$1,378 -$1,771 -$1,929 -$2,017 -$1,727 -$1,244 $272 -$785 -$525 -$17 $127 -$211 $53 $368 $811 $1,246 $1,094 $952 $704 $273 -$163 -$746 -$925 -$1,336 -$8,873
Total Performance of Wind Facility at $25/MWh Strike Price against 2015 ERCOT North Prices

Figure 7 - Budget Impact of VPPA with Indexed Electricity Purchasing
As Figure 7 demonstrates, our model facility would have seen significant volatility in their energy budget
if their purchases were fully indexed without a VPPA, capturing the value of low market prices in 2012
and 2015, and exposed to higher prices in 2011 and 2014. The maximum deviation in their budget, prior
to the VPPA, would have been $91,818, nearly 77% of their lowest annual expenditure. Adding exposure
to the same real-time market prices through the VPPA, however, mitigates such swing in budget, and
would reduce the maximum deviation in budget to $17,720. In fact, from 2012 to 2015 there would be no
more than $2,400 deviation in year-on-year budget, making it much easier for facility managers and
controllers to budget the energy line-item. Note that these totals include only the wholesale component of
the utility bill, and do not account for charges based on transmission and distribution, including capacity
and local utility peak demand charges.
However, many organizations with facilities in competitive electricity markets have already designed
purchasing strategies designed to improve budget certainty, such as buying fixed-price power or
purchasing blocks of power to reduce their exposure to market price swings. To analyze the impact of
layering a VPPA on top of such existing strategies, we assume that the same facility purchased a fixed-
price contract for power at a wholesale rate equal to the weighted average of monthly forward prices at
the date of purchase, and that the facility signs a calendar-year contract three months prior to the start of
each calendar year. Based on a 57% percentage of annual consumption attributed to on-peak hours, we
estimate that the contracted rates for calendar year energy supply contracts would be $35.61/MWh,
$38.25/MWh and $39.37/MWh for delivery in 2013, 2014, and 2015, respectively.
-$100
-$50
$0
$50
$100
$150
$200
$250
2011 2012 2013 2014 2015
Thousands
Indexed Annual Energy Cost
Wind PPA Cost ($25/MWh Strike)
Net Energy Spend (Excluding capacity, transmission, and demand charges)

Figure 8 - Summary of Estimated Calendar Year Fixed Energy Supply Contract Rate,
based on On-Peak and Off-Peak Monthly Forward Prices Observed Three Months Prior.
Source: ICE via Morningstar
Using these estimated fixed contract rates, we can assess the impact of the previously modeled virtual
power purchase agreement for wind generation at a $25/MWh strike price. As Figure 9 shows, the ability
of a virtual power purchase agreement to provide budget certainty for a given set of facilities or loads is
greatly diminished when such facilities or loads are served by fixed-rate electricity contracts. By contrast
to the small $2,400 deviation in budget seen when our modeled facility was purchasing power on the spot
market index, the same facility purchasing power under fixed rate contracts could see a 51% increase in
energy budget after contracting for a virtual power purchase agreement. This is primarily driven by the
fixed contracted rate being above market prices, which limits the ability for gains from the virtual power
purchase agreement to offset increases in energy spend.
Estimated Calendar Year Fixed Energy Price
Date contracted 8/31/2012 8/30/2013 8/30/2014
Contract Start Date 1/1/2013 1/1/2014 1/1/2015
Contract Rate ($/MWh) $35.61 $38.25 $39.37
Off-Peak Monthly Forward Prices, ERCOT North Hub
Forward date 8/31/2012 8/30/2013 8/30/2014
Contract Year 2013 2014 2015
Jan $26.10 $29.05 $33.20
Feb $26.25 $29.07 $33.01
Mar $23.35 $27.46 $31.40
Apr $23.30 $27.02 $29.93
May $24.05 $28.32 $28.76
Jun $27.43 $31.06 $30.22
Jul $32.74 $36.41 $31.81
Aug $32.93 $36.57 $35.58
Sep $26.36 $29.29 $29.58
Oct $22.80 $27.28 $28.43
Nov $23.54 $27.81 $28.57
Dec $24.88 $28.90 $29.71
On-Peak Monthly Forward Prices, ERCOT North Hub
Observed date 8/31/2012 8/30/2013 8/30/2014
Contract Year 2013 2014 2015
Jan $35.48 $35.92 $42.72
Feb $35.51 $35.94 $42.63
Mar $33.98 $37.08 $41.90
Apr $33.91 $36.48 $40.71
May $34.94 $37.37 $38.27
Jun $46.19 $47.55 $47.72
Jul $67.55 $63.54 $60.92
Aug $86.46 $89.57 $78.93
Sep $41.94 $42.85 $43.20
Oct $33.65 $37.08 $37.59
Nov $30.72 $35.47 $36.93
Dec $32.26 $35.81 $37.61

Figure 9 - Net Energy Spend for Different Energy Supply Purchasing Strategies
Combined with a Virtual Power Purchase Agreement
In conclusion, we see that the ability of a virtual power purchase agreement to improve budget certainty is
contingent on the purchaser having corresponding exposure to the spot market in which the VPPA is
settled. In the extreme case where an organization has purchased a VPPA corresponding to 100% of its
load in a particular market in order to improve budget certainty, the organization must consider allowing
some of the power purchases for its facilities to float the index market price, which would have the effect
of off-setting gains or losses from the VPPA.
2.1.5. Consideration of Cross-Market Risk of a VPPA
One primary benefit of a virtual power purchase agreement is that it does not involve the physical
delivery of power, allowing corporations to invest in alternative energy projects with the best economics,
not necessarily those located closest to their load. An organization could conceivably meet a 100%
renewable energy goal with one large virtual PPA in Texas, even if the majority of its load is centered in
Ohio, assuming it takes the environmental benefits of such a project.
Despite the seeming attractiveness of this approach to many organizations seeking to meet significant
renewable energy goals in a short timeframe, purchasing alternative energy through a VPPA in different
grid regions from the underlying electricity load can negate the ability of the VPPA to improve budget
certainty.
While underlying fundamentals like the price of natural gas play a role in guiding the prices in most US
electricity markets, many other local factors such as transmission and pipeline constraints, large plant
retirements, and changes in the supply resources mix have a greater impact on LMPs in particular regions.
An organization with demands for electricity in Ohio could see the impact of new transmission lines in
Ohio reflected in their energy spend, while the output of a wind asset in north Texas might not see any
deviation in price from the same event.
$-
$50
$100
$150
$200
$250
2013 2014 2015
Thousands
Net Energy Spend Inclusive of
Virtual Power Purchase Agreement
Indexed Energy Purchasing + VPPA Fixed Contract Rate + VPPA

Figure 10 - Net Energy Spend after Adding the Same VPPA Gain/Loss, with Loads
Centered in Different Grid Regions
To assess the impact of the imperfect correlation between prices in regional electricity grids, we consider
the net energy spend for the previously modeled facility and wind virtual power purchase agreement in
ERCOT North, but now assume the facility is purchasing power on the PJM West Hub real-time index
market. As Figure 10 shows, the range of potential budget outcomes varies much more significantly when
the VPPA is settled in a region outside of the load zone for the buyer’s energy demand. Though the
average spend for this facility would have been higher simply because PJM West Hub prices exceeded
those of ERCOT North Hub for the electricity demanded, the range between the 25% percentile and 75%
percentile of energy spend from 2011 to 2015 would also be 122% higher for the facility purchasing at
PJM West Hub as well. Thus, an organization seeking budget certainty as a primary benefit of their
VPPA should ensure that prices for the market in which their load is located are sufficiently well
correlated with the market in which the VPPA is settled.
2.1.6. Consideration of “Shape Risk” and Future Price Changes on a VPPA
As mentioned above, predicting hourly price scenarios for future years is exceedingly difficult and subject
to a variety of changing market and regulatory drivers. However, because the net benefit of the VPPA
deal to the buyer is a function of both market price and the time of production, any organization
considering a virtual power purchase agreement for a long term should consider the possible impacts to
hourly prices in the market over the coming years.
For example, wholesale prices in California have changed dramatically over the past five years, largely
due to a change in overall demand for power during the middle of the day as more solar generation has
been installed on the grid. As Figure 11 shows, the average price on the day-ahead electricity market at
the hour ending at 3:00 PM in May declined from over $35/MWh in 2012 to under $20/MWh in 2016. A
customer with exposure to such prices through a VPPA will certainly be impacted by such changes, both
positively and negatively. For instance, a VPPA with a generating facility producing more of its power in
the evening hours could stand to benefit from these changes to California’s pricing profile, if prices
continued to increase in the time periods immediately following sunset.
$120 $130 $140 $150 $160 $170 $180
Load & Generation in ERCOT
Load in PJM, Generation in ERCOT
Thousands
Variance in Net Energy Spend after VPPA
2011 to 2015

Figure 11 - Day-Ahead Average Hourly Electricity Prices at SP-15 (CAISO), May 2012 and
May 2016. Source: SparkLibrary, based on data from CAISO
3. Conclusions
We have demonstrated that consideration of a virtual power purchase agreement should involve not only
an analysis of average annual forward prices against the VPPA, but also a review of performance under
different hourly market price scenarios and a range of portfolio electricity purchasing strategies. The
major drivers of VPPA performance include the expected production profile of the generating asset, the
hourly pricing profile of the electricity market at which such a contract settles, the profile of the buyer’s
corresponding load, the hourly pricing profile of the market at which the buyer purchases electricity, and
the strategy with which the buyer continues to purchase power.
Table 1 - Summary of Market Risk Impacts to Virtual Power Purchase Agreement
Risk Factor Impact on VPPA Gain/Loss Impact on Net Energy Spend
Correlation of time of
generation to hourly
pricing
VPPA will have more gains when
power produced when market price
exceeds strike price, losses when
market price below strike price
None, if perfect correlation between
generation and load, and load
purchased at index price.
Correlation of time of
generation to demand for
power
None Less correlation between time of
production and demand for power
could increase deviation in budget
Correlation of market
prices for generation and
load
None Less correlation between market
prices at generation and load could
increase deviation in budget
Purchasing strategy for
corresponding load
(indexed or fixed)
None Less exposure to the market price
where VPPA settles could increase
deviation in budget

Risk Factor Impact on VPPA Gain/Loss Impact on Net Energy Spend
Future shape of pricing
curve changes
VPPA will have more gains when
power produced when market price
exceeds strike price, losses when
market price below strike price
None, if perfect correlation between
generation and load, and load
purchased at index price.
The above Table 1 describes a summary of potential market and budget risks associated with virtual
power purchase agreement, as well as their impact on the ability of the VPPA to return positive cash flow
to the buyer and its ability to reduce deviation in annual energy budgets. This list is not a comprehensive
review of all potential risks facing an investment decision in the virtual power purchase agreement, but
should inform a buyer about potential impacts of their investment on their energy management strategy.
The virtual power purchase agreement has emerged as a significant tool at the disposal of energy sourcing
departments across the largest organizations in North America in order to meet large alternative energy
purchasing goals. While the contract model has significant benefits to the buyer in its ability to remove
the complexities of physical delivery of power, buyers should be aware of the market risks entailed by
adding a virtual power purchase agreement into their energy portfolio. Used in concert with a calibrated
approach to energy sourcing, virtual power purchase agreements have the potential to allow corporations
make significant contributions in the deployment of alternative energy while meeting their cost and risk
objectives.
4. Abbreviations and Definitions
4.1. Abbreviations
PPA power purchase agreement
VPPA virtual power purchase agreement
LMP locational marginal price
REC renewable energy credit
MW megawatt, a unit of electric power
MWh megawatt-hour, a unit of electric energy
ERCOT Electricity Reliability Council of Texas
PJM PJM Interconnection, LLC
4.2. Definitions
Power purchase agreement A bilateral agreement in which a buyer (utility or end-use customer)
agrees to pay for the energy output from an electricity generating
facility and, optionally, the environmental attributes from said energy,
Virtual/synthetic power
purchase agreement
A type of power purchase agreement in which the buyer agrees to pay
a certain price for the output from an electricity generating asset, but
does not take title to the energy generated.
Contract for difference A contract structure, commonly used in a virtual power purchase
agreement, whereby two parties agree that the seller will pay the buyer
the difference between the current value of an asset and its value at the

time of contract signature (a “strike price”). If the difference is
negative, the buyer pays the seller.
Buyer The party that agrees to pay a specified price for the generation of
power
Seller The party that agrees to generate power in return for a specified price
Load The demand for energy from an end-use facility or customer
Forward price The price today (or on a specific day in the past) for the delivery of a
commodity to a specific location on a specified date in the future
Strike price The benchmark price agreed upon as a reference against which to
calculate contracts for difference
Locational marginal price The price of electricity delivered or produced at a specific node on the
electricity grid, inclusive of the cost of transmission congestion
5. Bibliography and References
Annual Energy Outlook 2015; United States Energy Information Administration, 2015.
https://www.eia.gov/forecasts/aeo/index.cfm
Corporate renewable energy procurement survey insights; PwC, June 2016.
https://www.pwc.com/us/en/corporate-sustainability-climate-change/publications/assets/pwc-corporate-
renewable-energy-procurement-survey-insights.pdf
Corporate Energy Sourcing: A New Engine for Renewables; Teresa A. Hill and William H Holmes,
Law360, 2015. http://www.klgates.com/files/Publication/bf40db4c-ff55-4d46-a858-
7fb37ae35a4b/Presentation/PublicationAttachment/2219715e-a9c8-4e1f-9bb7-
8828c505eb1d/Corporate_Energy_Sourcing_A_New_Engine_For_Renewables.pdf
Google’s Green PPAs: What, How, and Why; Google, Inc., September 17, 2013. Revision 3 (initially
published April 21, 2011).
http://static.googleusercontent.com/external_content/untrusted_dlcp/cfz.cc/en/us/green/pdfs/renewable-
energy.pdf
Historical Real Time Market Load Zone and Hub Prices; Electricity Reliability Council of Texas, 2015.
http://mis.ercot.com/misapp/GetReports.do?reportTypeId=13061&reportTitle=Historical%20RTM%20L
oad%20Zone%20and%20Hub%20Prices&showHTMLView=&mimicKey
Primer on LMP; Paul Arsuaga, R.W. Beck, Inc., Electric Light & Power, 2002.
http://www.elp.com/articles/print/volume-80/issue-12/power-pointers/primer-on-lmp.html
“Solar to Wreck Economics of Existing Power Markets”; Alex Gilbert, SparkLibrary, 2016.
https://www.sparklibrary.com/solar-wreck-economics-existing-power-markets/
“The Renewable Jumble: Basic Legal Considerations for Corporate End-Users of Renewable Energy,”
Frank Shaw and Ethan Schultz; Skadden, Arps, Slate, Meagher & Flom LLP. Corporate Renewable
Energy Procurement: Industry Insights; American Council On Renewable Energy, Corporate
Procurement Working Group, June 2016.

https://www.skadden.com/sites/default/files/publications/The_Renewable_Jumble_Basic_Legal_Consider
ations_for_Corporate_End_Users_of_Renewable_Energy.pdf

Evaluating Alternate Broadband Architectures
for Energy Consumption
A Video Service Perspective
Tom Gonder, Chief Network Architect, Charter Communications,
Broomfield, CO 80021
tom.gonder@charter.com
John B. Carlucci, Consultant, Petrichor Networks, LLC.
Boulder, CO 80304
jcarlucci@petrichornetworks.com
(719) 644-6361

Table of Contents
Title Page Number
1. Introduction ___________________________________________________________________ 52
2. Discussion____________________________________________________________________ 52
2.1. Consumption Consideration in System Design _________________________________ 52
2.2. Television Service _______________________________________________________ 53
2.3. Television Service Based Energy Function ____________________________________ 53
2.4. Ratings Based Usage Modeling_____________________________________________ 54
2.5. Data Collection Description of Television Consumption Behavior___________________ 56
2.6. IP Video Architecture _____________________________________________________ 61
2.7. Applying Usage Model to an IP Video Architecture ______________________________ 62
3. Conclusion____________________________________________________________________ 63
4.1. Abbreviations ___________________________________________________________ 64
4.2. Definitions______________________________________________________________ 64
List of Figures
Title Page Number
Figure 1 - Viewed Channels vs. Offered Channels (Weber and Gong) 55
Figure 2 - Histogram of Viewership across networks based on Nielsen data for US Population 55
Figure 3 - Peak Power Costs Relative to Prime Time Viewing 56
Figure 4 - Viewership across networks for a geography A (Peak 450,000) 57
Figure 5 - Viewership across networks for a geography B (Peak 225,000) 58
Figure 6 - Daily variation of viewership for across time within a geography A (Tuesday , Peak 450,000)
59
Figure 7 - Daily variation of viewership for across time within geography B (Tuesday , Peak 225,000) 60
Figure 8 - Daily variation of viewership for across time within geography A (Sunday , Peak 450,000) 61
Figure 9 - Representation of IP video architecture 62
Figure 10 - ECS for Simple IP System (Tuesday, Peak 450,000) 63

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